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[Tests] Skip lm_head embedding test #2352

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[Tests] Skip lm_head embedding test #2352

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9 changes: 7 additions & 2 deletions tests/llmcompressor/transformers/compression/test_compress_tensor_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,12 +216,17 @@ def test_quant_model_reload(format, dtype, tmp_path):
dense_tensor = og_state_dict[key].to(device)
reconstructed_tensor = reconstructed_state_dict[key].to(device)
assert dense_tensor.dtype == reconstructed_tensor.dtype
# Skip LM Head weight for now
# Note that the embedding is quantized
# TODO(@kylesayrs): this is a manifestation not using proper save context
if key == "lm_head.weight":
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@gemini-code-assist gemini-code-assist bot Feb 11, 2026

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medium

Hardcoding "lm_head.weight" makes this test brittle and dependent on the specific model's architecture. To make it more robust, consider dynamically identifying the output embedding layer's weight key.

You could achieve this by adding the following logic before the loop:

lm_head_key = None
if hasattr(model, "get_output_embeddings"):
    output_embeddings = model.get_output_embeddings()
    if output_embeddings is not None and hasattr(output_embeddings, "weight"):
        for name, param in model.named_parameters():
            if param is output_embeddings.weight:
                lm_head_key = name
                break

And then using lm_head_key in the comparison:

if key == lm_head_key:
    continue

This would make the test more resilient to changes in the model architecture or if other models are used in this test in the future.

continue
if key.endswith("weight") and format != "dense":
# we don't expect an exact match for compressed
diff = torch.abs(dense_tensor - reconstructed_tensor)
assert not torch.any(diff > 0.01).item()
assert not torch.any(diff > 0.01).item(), key
else:
assert torch.equal(dense_tensor, reconstructed_tensor)
assert torch.equal(dense_tensor, reconstructed_tensor), key
if os.path.isdir(tmp_path):
shutil.rmtree(tmp_path)

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